This paper considers the problem of tracking a moving target with a radio transmitter using an aerial robot in an online manner. The aerial robot is equipped with a low‐cost directional antenna and Software Defined Radio receiver to obtain the signal emitted by the target. The aerial robot rotates around itself and collects a predefined number of signal recordings from each direction to determine the bearing angle to the target in which the received signal strength is maximized. The measurement uncertainty is assumed to be bounded and represented by two triangular areas divided by a bisector. To localize and track the target, a particle filter‐based approach is proposed. In this approach, we integrate the discrete and bounded measurement model with the particle filter in such a way that the particles' weights are updated based on a novel method which considers the measurement wedge and the particle locations with respect to this wedge along with a logistic function. We also incorporate the doubling strategy into the particle filter to determine the next measurement locations and avoid arbitrarily large number of measurements. We choose wildlife monitoring as a use case scenario in which a radio transmitter is put on the animal under consideration to allow wildlife researchers to track it. Since each animal has its own motion behavior, we consider different motion models for the target, which are used in modeling animal movements in wildlife studies. Therefore, the proposed approach is validated using a target moving with varying velocity and acceleration. We verified the tracking performance of the approach through a series of extensive simulations. We compared the proposed approach with the optimal offline strategy in terms of the empirical competitive ratio of the total distance traveled and the tracking distance. We also developed a low‐cost hardware platform and software infrastructure for the proposed tracking system. Using this platform, we conducted field experiments for the stationary and moving targets.